Forecasting the yield curve: the role of additional and time‐varying decay parameters, conditional heteroscedasticity, and macro‐economic factors

IF 1.2 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
João F. Caldeira, Werley C. Cordeiro, Esther Ruiz, André A.P. Santos
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引用次数: 0

Abstract

In this article, we analyse the forecasting performance of several parametric extensions of the popular Dynamic Nelson–Siegel (DNS) model for the yield curve. Our focus is on the role of additional and time‐varying decay parameters, conditional heteroscedasticity, and macroeconomic variables. We also consider the role of several popular restrictions on the dynamics of the factors. Using a novel dataset of end‐of‐month continuously compounded Treasury yields on US zero‐coupon bonds and frequentist estimation based on the extended Kalman filter, we show that a second decay parameter does not contribute to better forecasts. In concordance with the preferred habitat theory, we also show that the best forecasting model depends on the maturity. For short maturities, the best performance is obtained in a heteroscedastic model with a time‐varying decay parameter. However, for long maturities, neither the time‐varying decay nor the heteroscedasticity plays any role, and the best forecasts are obtained in the basic DNS model with the shape of the yield curve depending on macroeconomic activity. Finally, we find that assuming non‐stationary factors is helpful in forecasting at long horizons.
预测收益率曲线:附加和时变衰减参数、条件异方差和宏观经济因素的作用
在本文中,我们分析了针对收益率曲线的流行动态尼尔森-西格尔(Dynamic Nelson-Siegel,DNS)模型的几种参数扩展的预测性能。我们的重点是附加和时变衰减参数、条件异方差和宏观经济变量的作用。我们还考虑了几种流行的因素动态限制的作用。利用美国零息债券月末连续复利国债收益率的新数据集和基于扩展卡尔曼滤波器的频繁估计,我们表明第二个衰减参数无助于获得更好的预测。与首选栖息地理论一致,我们还表明最佳预测模型取决于期限。就短期而言,具有时变衰减参数的异方差模型的性能最佳。然而,对于长期限而言,时变衰减和异方差都不起任何作用,最佳预测是在收益率曲线形状取决于宏观经济活动的 DNS 基本模型中获得的。最后,我们发现假设非稳态因素有助于进行长期预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Time Series Analysis
Journal of Time Series Analysis 数学-数学跨学科应用
CiteScore
2.00
自引率
0.00%
发文量
39
审稿时长
6-12 weeks
期刊介绍: During the last 30 years Time Series Analysis has become one of the most important and widely used branches of Mathematical Statistics. Its fields of application range from neurophysiology to astrophysics and it covers such well-known areas as economic forecasting, study of biological data, control systems, signal processing and communications and vibrations engineering. The Journal of Time Series Analysis started in 1980, has since become the leading journal in its field, publishing papers on both fundamental theory and applications, as well as review papers dealing with recent advances in major areas of the subject and short communications on theoretical developments. The editorial board consists of many of the world''s leading experts in Time Series Analysis.
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